Native Android development in Kotlin and Jetpack Compose: fast on every device tier, correct on every screen size, and shipped through Play review and staged rollouts.
We build native Android apps in Kotlin, with Jetpack Compose as the default UI toolkit. Native gives you the full platform: Material 3 done properly, new Android APIs the day they ship, and enough performance headroom to run well on flagships and on the mid-range devices most of the market actually uses.
Android's defining constraint is device diversity: thousands of models, a wide spread of chipsets and memory, and screens from compact phones to foldables and tablets. We treat that as an engineering requirement from day one: adaptive Compose layouts, baseline profiles for fast startup, R8 optimization, and testing on a device matrix built from your users' actual analytics, not from whatever happens to be on the desk. If cross-platform would serve you better, we say so early; we build with React Native and Flutter too, so the advice is not self-serving.
Delivery runs through Google Play the way Google intends: internal and closed testing tracks from the first sprint, pre-launch reports on real devices, an accurate data safety declaration, and a staged rollout that expands from a few percent to full release only while crash and ANR rates hold. We also keep the app compliant after launch, target API level deadlines included, so it does not get delisted a year later.
Jetpack Compose and Material 3 produce interfaces that behave the way Android users expect: predictive back, dynamic color, correct notifications, and adaptive layouts for foldables and tablets, right by construction rather than approximated.
Kotlin compiled ahead of time, baseline profiles, R8 shrinking and startup budgets, verified with Macrobenchmark on low-end hardware as well as flagships. Smooth on the devices your users actually own.
ML Kit covers text recognition, barcodes, translation and face detection out of the box; LiteRT runs your custom models; Gemini Nano adds on-device generative AI on supported hardware. Features work offline, respond fast and keep data on the phone.
Widgets, app shortcuts, foreground services done correctly, and each Android release's APIs available immediately, without waiting for a cross-platform framework to expose them.
We set the minimum SDK from your users' real device data, then design the architecture: Kotlin, Compose, coroutines and Flow, and a modular Gradle setup your own engineers can extend.
Screens follow Material 3 and Android conventions from the first wireframe, with adaptive layouts for phones, foldables and tablets specified up front, not retrofitted.
Weekly builds on a closed Play track from the first sprint: you review progress on your own device, and feedback lands in the next build. CI runs unit and instrumented tests plus static analysis on every merge.
We prepare the store listing and data safety form, run pre-launch reports across the device matrix, then release in stages, watching crash and ANR rates at each step before expanding to 100 percent.
When the app is core to your business, when performance on mid-range devices decides retention, or when you need deep platform integration: background work, Bluetooth, camera pipelines, widgets, on-device ML. For a content app or a demand-testing MVP on both platforms, cross-platform is often the better spend; we tell you which case you are in during the first call.
With data, not guesswork. We pull the device and OS distribution from your analytics (or from market data for your region if you have none yet), define a test matrix that covers it, and set the minimum SDK where the user loss is negligible. Adaptive layouts, baseline profiles and pre-launch reports on real hardware do the rest.
Play review is usually faster than Apple's, but enforcement is continuous: target API level deadlines, data safety accuracy, and policy checks on every update. We keep the app compliant, release through internal and closed tracks first, then roll out in stages and expand only while crash and ANR rates hold. If a policy flag comes, the appeal and the fix are on us.
Text recognition, barcode and document scanning, translation, smart replies and custom LiteRT models run on virtually all devices; Gemini Nano adds on-device generative AI on supported hardware. It works offline, costs nothing per request and keeps data on the phone. For heavier generative features we route to server-side LLMs and combine both.
Book a free 30-minute call with a senior Android engineer. You leave with an honest read on native versus cross-platform for your case, and a rough scope.
Talk to an Android engineer